Abstract: Continuous emotion recognition based on multimodal physiological data plays an important role in many fields. However, it needs more physiological data to train emotion recognition models due to the lack of subjects’ data and subjectivity of emotion, and it is largely affected by homologous subjects’ data. In this study, we propose multiple emotion recognition methods based on facial expressions and EEG. Regarding the modality of facial images, we propose a multi-task convolutional neural network trained by transfer learning to avoid over-fitting induced by small datasets of facial images. With respect to the modality of EEG, we propose two emotion recognition models. The first is a subject-dependent model based on support vector machine, possessing high accuracy when the validation and training data are homogeneous. The second is a cross-subject model for reducing the impact caused by the individual variation and non-stationarity of EEG. It is based on a long short-term memory network, performing stably under the circumstance that validation and training data are heterogeneous. To improve the accuracy of emotion recognition for homogeneous data, we propose two methods for decision-level fusion of multimodal emotion prediction: Weight enumeration and adaptive boost. According to the experiments, when the validation and training data are homogeneous, under the best circumstance, the average accuracy that multimodal emotion recognition models reached in both arousal and valence dimensions were 74.23% and 80.30%; as the validation and training data are heterogeneous, the accuracy that the cross-subject model reached in both arousal and valence dimensions are 58.65% and 51.70%.
Abstract: As the widespread application of information technologies in Industrial Control System (ICS), ICSs have gradually transformed from closed systems to open and interconnected ones, encountering with the challenges to network security. This paper elaborates on the current situation of information security in ICS through security events. Then, it focuses on the ICS’s architecture and the differences between ICS information security and traditional network security. Moreover, it systematically analyzes the proceedings of the ICS & SCADA Cyber Security Research 2018 (ICS-CSR 2018). Besides, it classifies and examines the proposed security solutions regarding the system architecture and communication protocols. Finally, drawing on the current solutions and in response to actual requirements, this paper summarizes three key directions: network attack models, the ICS’s simulation platforms, and the non-technical Human-Machine Interface (HMI) technology.
Abstract: Static hand gesture recognition based on multi-feature weighted fusion is proposed to solve the problems of singularity and omission in convolutional neural network for feature extraction. Firstly, the Fourier and Hu moments of the segmented gesture image are extracted and fused as the local features. Besides, a dual-channel convolutional neural network is designed to extract the deep features of the gesture image, which are further treated by dimensionality reduction by principal component analysis. Secondly, the extracted local and deep features are weighted and fused as effective description for hand gesture recognition. Finally, gesture images are classified with Softmax classifier. Experimental results verify the proposed method, and the recognition accuracy reaches over 99% on the image dataset.
Abstract: A trajectory tracking control scheme based on an improved nonlinear disturbance observer is presented to address the position and attitude errors caused by inaccurate modeling and vulnerability to external disturbances of wall-climbing robots. First, a kinematic controller is designed through back stepping control to provide reference centroid velocity and angular velocity for dynamic control of robots. Secondly, an improved nonlinear disturbance observer serves as a feed forward controller to estimate modeling errors and external disturbances of the dynamic model, ensuring exponential convergence of disturbance errors. Finally, a sliding mode controller is designed based on the dynamic model with an interference observer. The scheme compensates for external disturbances quickly and its stability is proven by Lyapunov’s theorem. The simulation results demonstrate that the control scheme performs well in avoiding modeling errors and external interferences.
Abstract: Singular Value Decomposition (SVD) is adopted for image compression of the data matrix to obtain an optimal compression ratio and a clear compressed image. The principle of SVD and its application to compressing images are elaborated. Two methods for obtaining the better number of eigenvalues are proposed including the ratio threshold of eigenvalue number and the ratio threshold of eigenvalue sum. The experiments reveal that when the ratio threshold of eigenvalue number is 0.1, a clear image is obtained with the compression ratio of 5.99. When the ratio threshold of eigenvalue sum is 0.85, a clear image is also acquired with the compression ratio for PNG images of 7.89 and that for JPG images of 5.92. Case study indicates that the first 1% of eigenvalues represent more data characteristics. When the ratio threshold of eigenvalue number is determined, the compression ratios for PNG and JPG images are identical. When the ratio threshold of the eigenvalue sum is determined, the compression ratio for PNG images is higher than that for JPG images. The method for obtaining the eigenvalue number according to the ratio threshold of eigenvalue sum is more universal. It can be applied to solving alpha channel redundancy and setting a unified ratio threshold of eigenvalue sum for large-scale image compression.
Abstract: At the age of globalization, as the increase in the personnel mobility rate, the transmission modes and speed of epidemic diseases go far beyond those in the past. The basic principles and methods of building the personnel monitoring and management system based on the blockchain technology were expounded. When a major infectious disease attacks, the people from the epidemic area and confirmed or suspected patients are monitored in real time. The system can inquire into the history of personnel mobility, find close contacts, and search for the sources of infection, which can support isolation measures for epidemic prevention and control.
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